nag_corr_cov (g02bxc) Example Program Results
Case 1 --- Using weights
Input data
0.9 0.1 0.9 9.1
0.0 0.0 0.1 3.7
0.1 1.3 0.4 4.5
Sample means.
0.5
0.4
0.6
Standard deviation.
0.4
0.6
0.3
Correlation matrix.
1.0000 -0.4932 0.9839
-0.4932 1.0000 -0.3298
0.9839 -0.3298 1.0000
Variance matrix.
0.197 -0.123 0.149
-0.123 0.316 -0.063
0.149 -0.063 0.117
Sum of weights 17.3
Case 2 --- Using weights
Input data
9.1 3.7 4.5 0.1
0.9 0.1 0.9 1.3
0.0 0.0 0.1 0.4
Sample means.
1.3
0.3
1.0
Standard deviation.
3.3
1.4
1.5
Correlation matrix.
1.0000 0.9908 0.9903
0.9908 1.0000 0.9624
0.9903 0.9624 1.0000
Variance matrix.
10.851 4.582 5.044
4.582 1.971 2.089
5.044 2.089 2.391
Sum of weights 1.8
Case 3 --- Not using weights
Input data
1.1 0.1 9.7 0.7
11.1 23.5 11.1 9.4
0.9 9.0 8.7 0.0
Sample means.
4.4
10.9
9.8
Standard deviation.
5.8
11.8
1.2
Correlation matrix.
1.0000 0.9193 0.9200
0.9193 1.0000 0.6915
0.9200 0.6915 1.0000
Variance matrix.
33.951 63.208 6.485
63.208 139.250 9.871
6.485 9.871 1.464
Sum of weights 3.0
Case 4 --- Using weights
Input data
1.1 0.1 9.7 0.7
11.1 23.5 11.1 19.4
0.9 9.0 78.7 0.0
Sample means.
10.7
22.7
11.1
Standard deviation.
1.9
4.5
1.8
Correlation matrix.
1.0000 0.9985 0.0173
0.9985 1.0000 0.0716
0.0173 0.0716 1.0000
Variance matrix.
3.672 8.538 0.059
8.538 19.909 0.570
0.059 0.570 3.185
Sum of weights 20.1
Case 5 --- Not using weights
Input data
1.1 0.1 9.7 0.7
11.1 3.5 13.1 19.4
0.9 0.0 78.7 0.0
Sample means.
4.4
1.2
33.8
Standard deviation.
5.8
2.0
38.9
Correlation matrix.
1.0000 0.9999 -0.4781
0.9999 1.0000 -0.4881
-0.4781 -0.4881 1.0000
Variance matrix.
33.951 11.567 -108.343
11.567 3.941 -37.687
-108.343 -37.687 1512.750
Sum of weights 3.0
Case 6 --- Using weights
Input data
1.1 0.1 9.7 0.7
17.1 93.5 13.1 19.4
30.9 0.0 78.7 0.9
Sample means.
17.2
86.3
15.9
Standard deviation.
4.2
25.6
13.7
Correlation matrix.
1.0000 -0.0461 0.7426
-0.0461 1.0000 -0.7033
0.7426 -0.7033 1.0000
Variance matrix.
17.846 -4.989 43.123
-4.989 656.407 -247.692
43.123 -247.692 188.970
Sum of weights 21.0